import pandas as pd 
import numpy as np
import matplotlib.pyplot as plt

from sklearn.model_selection import GridSearchCV, RandomizedSearchCV, train_test_split, TimeSeriesSplit
from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import StandardScaler, PolynomialFeatures
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression

from catboost import CatBoostRegressor
from lightgbm import LGBMRegressor, plot_importance

from statsmodels.tsa.seasonal import seasonal_decompose

taxi = pd.read_csv('/home/panther/Documents/Deep Learning/datasets/taxi.csv', parse_dates=[0], index_col=[0])

taxi.head()

taxi.isna().sum()

num_orders    0
dtype: int64

taxi.info()

<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 26496 entries, 2018-03-01 00:00:00 to 2018-08-31 23:50:00
Data columns (total 1 columns):
 #   Column      Non-Null Count  Dtype
---  ------      --------------  -----
 0   num_orders  26496 non-null  int64
dtypes: int64(1)
memory usage: 414.0 KB

taxi.index.is_monotonic_increasing

True

taxi = taxi.resample('1h').sum()
taxi.shape

(4416, 1)

figsize = (12, 6)
target_col = 'num_orders'
min_date = taxi.index.min()
max_date = taxi.index.max()

print(f'Левая граница: {min_date}')
print(f'Правая граница: {max_date}')
print(f'Длина ряда: {max_date - min_date}')

Левая граница: 2018-03-01 00:00:00
Правая граница: 2018-08-31 23:00:00
Длина ряда: 183 days 23:00:00

ax = taxi.plot(title='Общий график количества вызовов такси по времени', figsize=figsize)
taxi.rolling(50).mean().plot(ax=ax, color='red', linewidth=1);
ax.legend(['Количество заказов в день', 'Скользящая средняя от количества заказов']);
ax.set_xlabel('Дата');

taxi.describe()

ax = taxi[target_col].plot.hist(bins=50, figsize=figsize);
mean = taxi[target_col].mean(); mode = taxi[target_col].mode()[0]
ax.axvline(mean, color='black', ls='--')
ax.axvline(mode, color='blue', ls='--')
ax.text(mean+5, 200, f'$\mu\ =\ {mean:.2f}\ (среднее)$')
ax.text(mode-35, 100, f'$\mu_o\ =\ {mode:.2f}\ (мода)$', color='blue'); 
ax.set(title='Распределение количества заказов', xlabel='Заказы', ylabel='Частота');

taxi.iloc[:taxi.shape[0]//2].agg(['mean', 'std'])

taxi.iloc[taxi.shape[0]//2:].agg(['mean', 'std'])

ax = taxi.resample('1d').sum().plot(figsize=figsize)
taxi.resample('1d').sum().rolling(10).mean().plot(ax=ax, color='red')
ax.set(title='Дневной график заказов такси', xlabel='Дата', ylabel='Количество')
ax.legend(['Количество заказов в день', 'Скользящая средняя от количества заказов']);

def decompose(data, period_days, plot=['Тренд', 'Сезонность', 'Остатки'], returning=False, mov_avg=0):
    
    # определение частотности ряда в типе timedelta и перевод периода (в днях) в период частоты ряда
    delta = data.iloc[:2].apply(lambda x: x.index[1] - x.index[0])[target_col]
    decomposed = seasonal_decompose(data, period=pd.to_timedelta(f'{period_days}d') // delta)
    
    dec = {'Тренд': decomposed.trend, 'Сезонность': decomposed.seasonal, 'Остатки': decomposed.resid}
    
    plt.figure(figsize=(figsize[0], figsize[1]*len(plot)))
    plt.subplots_adjust(hspace=0.3)
    
    for i, n in enumerate(plot): # в цикле динамически расширяется фигура для размещения графиков
        ax = plt.subplot(len(plot), 1, i+1)
        name = f' {n} (период окна декомпозиции: {period_days} дней)'
        dec[n].plot(title=name, ax=ax, xlabel='Дата', ylabel='Количество заказов')
        
        # если нужен график скользящей средней периода mov_avg
        if mov_avg > 0:
            dec[n].rolling(mov_avg).mean().plot(title=name, ax=ax, xlabel='Дата', ylabel='Количество заказов',
                                                color='red')
            
    # если нужно вернуть значения декомпозиции
    if returning:
        return dec['Тренд'], dec['Сезонность'], dec['Остатки']

decompose(taxi, period_days=30, plot=['Тренд'])

decompose(taxi, period_days=10, plot=['Тренд'])

decompose(taxi.resample('1d').sum(), period_days=30, plot=['Сезонность'], mov_avg=5)

decompose(taxi[min_date:min_date+pd.to_timedelta(30, unit='d')], 7, plot=['Сезонность'], mov_avg=5)

taxi.loc['2018-03-05'].index.day_name()[0]

'Monday'

decompose(taxi[max_date-pd.to_timedelta(14, unit='d'):max_date], 7, plot=['Сезонность'], mov_avg=5)

taxi.loc['2018-08-20'].index.day_name()[0]

'Monday'

decompose(taxi[min_date:min_date+pd.to_timedelta(2, unit='d')], 1, plot=['Сезонность'])

decompose(taxi[max_date-pd.to_timedelta(2, unit='d'):max_date], 1, plot=['Сезонность'])

taxi[target_col] = pd.to_numeric(taxi[target_col], downcast='integer')
taxi.info()

<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 4416 entries, 2018-03-01 00:00:00 to 2018-08-31 23:00:00
Freq: H
Data columns (total 1 columns):
 #   Column      Non-Null Count  Dtype
---  ------      --------------  -----
 0   num_orders  4416 non-null   int16
dtypes: int16(1)
memory usage: 172.2 KB

def make_features(data, max_lag, rolling_mean_size):
    '''Функция генерации новых признаков временного ряда'''
    data.loc[:, 'day_of_year'] = data.index.dayofyear.to_numpy().astype('int16')
    data.loc[:, 'day_of_week'] = data.index.dayofweek.to_numpy().astype('int8')
    data.loc[:, 'hour'] = data.index.hour.to_numpy().astype('int8')
    
    for lag in range(1, max_lag+1):
        data[f'lag_{lag}'] = data[target_col].shift(lag).interpolate().bfill().astype('int16')

    data['rolling_mean'] = data[target_col].shift().rolling(rolling_mean_size).mean().interpolate().bfill().astype('float32')

def finetune(model_name, parameters, features, target, verbose=3, jobs=-1, **kwargs):
    '''Фукнция подбора гиперпараметров
    На вход принимает класс модели, решетку гиперпараметров для поиска, признаки и целевой признак
    Возвращает лучшую из натренерованных моделей, её гиперпараметры и её луший результат по метрике RMSE
    '''
    cv = GridSearchCV(model_name(**kwargs), param_grid=parameters, scoring='neg_mean_squared_error',
                            cv=TimeSeriesSplit(n_splits=5), verbose=verbose, n_jobs=jobs)
    cv.fit(features, target)  
    
    return cv.best_estimator_, cv.best_params_, np.sqrt(-cv.best_score_)

def print_cv_results(params, rmse):
    '''Выводит результаты кросс-валидации на экран'''
    print()
    print(f'Лучшие параметры модели: {params}')
    print(f'(train, CV) лучшее RMSE: {rmse:.2f}')
    print()

params = {'learning_rate': [0.05449], 'max_bin': [40], 'num_leaves': [32]}
test_df = taxi.copy()
best_p = []
best_rmse = 9999

for lag in range(3, 30):
    for avg in range(7, 40):
        make_features(test_df, lag, avg)
        t_features = test_df.drop([target_col], axis=1)
        t_target = test_df[target_col]

        t_features_train, t_features_test, t_target_train, t_target_test = train_test_split(
            t_features, t_target, shuffle=False, test_size=0.1)
        test_df = taxi.copy()

        _, _, rmse = finetune(LGBMRegressor, params, t_features_train, t_target_train, verbose=0)

        if rmse < best_rmse:
            best_rmse = rmse
            best_p.append((lag, avg, best_rmse))
print(best_p)

[(3, 7, 24.196688282311555), (3, 8, 24.0252403101438)]

make_features(taxi, 3, 8)

taxi

features = taxi.drop([target_col], axis=1)
target = taxi[target_col]

features_train, features_test, target_train, target_test = train_test_split(features, target, shuffle=False,
                                                                            test_size=0.1)

print(f'Размер тренировочной выборки: {features_train.shape}')
print(f'Размер тестовоый выборки: {features_test.shape}')

Размер тренировочной выборки: (3974, 7)
Размер тестовоый выборки: (442, 7)

%%time

params = {}
lr_best_model, lr_best_params, lr_best_rmse = finetune(LinearRegression, params, features_train, target_train)
print_cv_results(lr_best_params, lr_best_rmse)

Fitting 5 folds for each of 1 candidates, totalling 5 fits

Лучшие параметры модели: {}
(train, CV) лучшее RMSE: 31.74

CPU times: user 6.98 ms, sys: 7.81 ms, total: 14.8 ms
Wall time: 132 ms

%%time 

params = {'max_depth': [20, 21],
          'n_estimators': [157, 158]}

rf_best_model, rf_best_params, rf_best_rmse = finetune(RandomForestRegressor, params,
                                                        features_train, target_train, random_state=42)
print_cv_results(rf_best_params, rf_best_rmse)

Fitting 5 folds for each of 4 candidates, totalling 20 fits

Лучшие параметры модели: {'max_depth': 21, 'n_estimators': 157}
(train, CV) лучшее RMSE: 24.27

CPU times: user 1.92 s, sys: 67.3 ms, total: 1.99 s
Wall time: 10.5 s

%%time

params = {
    'learning_rate': [0.0279, 0.028, 0.0281],
    'iterations': [1125, 1126, 1127],
    'l2_leaf_reg': [2]
    
}

cb_best_model, cb_best_params, cb_best_rmse = finetune(CatBoostRegressor, params,
                                       features_train, target_train, random_state=42)
print_cv_results(cb_best_params, cb_best_rmse)

Fitting 5 folds for each of 9 candidates, totalling 45 fits
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[CV 3/5] END iterations=1129, l2_leaf_reg=2, learning_rate=0.0281;, score=-513.332 total time=   3.8s
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/home/panther/anaconda3/lib/python3.9/site-packages/catboost/core.py:1411: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.
  self._init_pool(data, label, cat_features, text_features, embedding_features, embedding_features_data, pairs, weight,
/home/panther/anaconda3/lib/python3.9/site-packages/catboost/core.py:1411: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.
  self._init_pool(data, label, cat_features, text_features, embedding_features, embedding_features_data, pairs, weight,
/home/panther/anaconda3/lib/python3.9/site-packages/catboost/core.py:1411: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.
  self._init_pool(data, label, cat_features, text_features, embedding_features, embedding_features_data, pairs, weight,
/home/panther/anaconda3/lib/python3.9/site-packages/catboost/core.py:1411: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.
  self._init_pool(data, label, cat_features, text_features, embedding_features, embedding_features_data, pairs, weight,

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[CV 5/5] END iterations=1129, l2_leaf_reg=2, learning_rate=0.028;, score=-867.916 total time=   5.0s
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[CV 5/5] END iterations=1129, l2_leaf_reg=2, learning_rate=0.0281;, score=-872.997 total time=   2.8s
0:	learn: 32.9483244	total: 3.92ms	remaining: 4.4s
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/home/panther/anaconda3/lib/python3.9/site-packages/catboost/core.py:1411: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.
  self._init_pool(data, label, cat_features, text_features, embedding_features, embedding_features_data, pairs, weight,
/home/panther/anaconda3/lib/python3.9/site-packages/catboost/core.py:1411: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.
  self._init_pool(data, label, cat_features, text_features, embedding_features, embedding_features_data, pairs, weight,

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[CV 2/5] END iterations=1125, l2_leaf_reg=2, learning_rate=0.0279;, score=-498.520 total time=   2.5s
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/home/panther/anaconda3/lib/python3.9/site-packages/catboost/core.py:1411: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.
  self._init_pool(data, label, cat_features, text_features, embedding_features, embedding_features_data, pairs, weight,

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/home/panther/anaconda3/lib/python3.9/site-packages/catboost/core.py:1411: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.
  self._init_pool(data, label, cat_features, text_features, embedding_features, embedding_features_data, pairs, weight,

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/home/panther/anaconda3/lib/python3.9/site-packages/catboost/core.py:1411: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.
  self._init_pool(data, label, cat_features, text_features, embedding_features, embedding_features_data, pairs, weight,

%%time

params = {'learning_rate': [0.05449, 0.0545],
          'num_leaves': [32, 33, 34],
          'max_bin': [40]
}

lgbm_best_model, lgbm_best_params, lgbm_best_rmse = finetune(LGBMRegressor, params,
                                       features_train, target_train, random_state=42)

print_cv_results(lgbm_best_params, lgbm_best_rmse)

Fitting 5 folds for each of 6 candidates, totalling 30 fits

Лучшие параметры модели: {'learning_rate': 0.0545, 'max_bin': 40, 'num_leaves': 33}
(train, CV) лучшее RMSE: 23.87

CPU times: user 267 ms, sys: 4.24 ms, total: 271 ms
Wall time: 618 ms

training_stats = pd.DataFrame({'Модель': ['Линейная регрессия', 'Случайный лес', 'CatBoost', 'LightGBM'],
              '(train CV) RMSE': [round(x, 2) for x in [lr_best_rmse, rf_best_rmse, cb_best_rmse, lgbm_best_rmse]],
             }).set_index('Модель').sort_values(by='(train CV) RMSE')

training_stats

best_model = training_stats.index[0]
best_model

'LightGBM'

models = {'LightGBM': lgbm_best_model, 
          'CatBoost': cb_best_model,
          'Линейная регрессия': lr_best_model,
          'Случайный лес': rf_best_model}

pred_test = models[best_model].predict(features_test)
final_test_rmse = mean_squared_error(target_test, pred_test) ** 0.5

print(f'RMSE модели {best_model} на тестовых данных составил {final_test_rmse:.2f} заказов такси.')

RMSE модели LightGBM на тестовых данных составил 42.25 заказов такси.

plt.figure(figsize=figsize)
plt.plot(target_test.index, target_test, lw=0.8) 
plt.plot(target_test.index, pd.Series(pred_test, index=target_test.index), color='green', lw=1);
plt.title('Тестовая выборка'); plt.xlabel('Дата'); plt.legend(['Фактические заказы', 'Предсказанные заказы']);

plt.figure(figsize=(12, 6))
plt.plot(target_test['2018-08-25':'2018-08-29'].index, target_test['2018-08-25':'2018-08-29'], lw=0.8) 
plt.plot(target_test['2018-08-25':'2018-08-29'].index, 
pd.Series(pred_test, index=target_test.index)['2018-08-25':'2018-08-29'], color='green', lw=1);
plt.title('Тестовая выборка'); plt.xlabel('Дата'); plt.legend(['Фактические заказы', 'Предсказанные заказы']);

final_test_rmse < 48

True

training_stats

best_model

'LightGBM'

print(f'RMSE модели {best_model} на тестовых данных составил {final_test_rmse:.2f} заказов такси.')

RMSE модели LightGBM на тестовых данных составил 42.25 заказов такси.

final_test_rmse < 48

True

	num_orders
count	4416.000000
mean	84.422781
std	45.023853
min	0.000000
25%	54.000000
50%	78.000000
75%	107.000000
max	462.000000

Прогнозирование заказов такси¶

Подготовка данных¶

Загрузка библиотек¶

Загрузка и знакомство с данными¶

Выводы, подготовка данных¶

Анализ данных временного ряда¶

Распределение данных и стандартные статистики¶

Анализ сезонной декомпозиции ряда¶

Оценка трендовой компоненты ряда¶

Месячная сезонность¶

Недельная сезонность¶

Дневная сезонность¶

Выводы, анализ данных временного ряда¶

Обучение моделей¶

Линейная регрессия¶

Случайный лес¶

CatBoost¶

LightGBM¶

Выводы, обучение моделей¶

Тестирование моделей¶

Итоговый вывод¶

	num_orders
datetime
2018-03-01 00:00:00	9
2018-03-01 00:10:00	14
2018-03-01 00:20:00	28
2018-03-01 00:30:00	20
2018-03-01 00:40:00	32

	num_orders	day_of_year	day_of_week	day	hour	lag_1	lag_2	lag_3	rolling_mean
datetime
2018-03-01 00:00:00	124	60	3	1	0	124	124	124	52.750
2018-03-01 01:00:00	85	60	3	1	1	124	124	124	52.750
2018-03-01 02:00:00	71	60	3	1	2	85	124	124	52.750
2018-03-01 03:00:00	66	60	3	1	3	71	85	124	52.750
2018-03-01 04:00:00	43	60	3	1	4	66	71	85	52.750
...	...	...	...	...	...	...	...	...	...
2018-08-31 19:00:00	136	243	4	31	19	207	217	197	167.375
2018-08-31 20:00:00	154	243	4	31	20	136	207	217	163.500
2018-08-31 21:00:00	159	243	4	31	21	154	136	207	165.750
2018-08-31 22:00:00	223	243	4	31	22	159	154	136	164.875
2018-08-31 23:00:00	205	243	4	31	23	223	159	154	176.125

	(train CV) RMSE
Модель
LightGBM	23.87
CatBoost	24.15
Случайный лес	24.27
Линейная регрессия	31.74